Components
These docs are for version 1.x of Rasa Open Source.
User Guide
- Installation
- Rasa Tutorial
- Command Line Interface
- Architecture
- Messaging and Voice Channels
- Evaluating Models
- Validate Data
- Running the Server
- Running Rasa with Docker
- Cloud Storage
NLU
- About
- Using NLU Only
- Training Data Format
- Choosing a Pipeline
- Language Support
- Entity Extraction
- Components
Core
Conversation Design
API Reference
- Action Server
- HTTP API
- Jupyter Notebooks
- Agent
- Custom NLU Components
- Events
- Tracker
- Tracker Stores
- Event Brokers
- Training Data Importers
- Featurization
- Migration Guide
- Rasa Change Log
Migrate from (beta)
Reference
Versions
viewing: 1.2.9
Components
For clarity, we have renamed the pre-defined pipelines to reflect what they do rather than which libraries they use as of Rasa NLU 0.15. The tensorflow_embedding pipeline is now called supervised_embeddings, and spacy_sklearn is now known as pretrained_embeddings_spacy. Please update your code if you are using these.
This is a reference of the configuration options for every built-in component in Rasa NLU. If you want to build a custom component, check out Custom NLU Components.
Word Vector Sources
Featurizers
Intent Classifiers
Tokenizers
Entity Extractors
- MitieEntityExtractor
- SpacyEntityExtractor
- EntitySynonymMapper
- CRFEntityExtractor
- DucklingHTTPExtractor
Note
The MITIE library needs a language model file, that must be specified in the configuration:
pipeline:
- name: "MitieNLP"
model: "data/total_word_feature_extractor.dat"
For more information where to get that file from, head over to installing MITIE.
Short Description
MITIE initializer: Initializes mitie structures. Every mitie component relies on this, hence this should be put at the beginning of every pipeline that uses any mitie components.